# Revision history [back]

what optimisation algorithms are available.

Check those provided by SciPy's optimization and root finding library. All of them are available through the Sage interpreter by calling the appropriate scipy.optimize function. In some (but not all) cases, Sage provides an interface to these algorithms, so that you can directly use Sage symbolic functions as arguments.

It is worth mentioning that "out there" you'll find dozens of Python libraries for mathematical optimization. Notice that standard Python packages can be installed with the terminal command $sage --pip install --user package_name, and if the installation goes well, it is available the next time you start Sage via sage: import package_name. Feel free to provide a minimal example to get more precise feedback. what optimisation algorithms are available. Check those provided by SciPy's optimization and root finding library. All of them are available through the Sage interpreter by calling the appropriate scipy.optimize function. In some (but not all) cases, Sage provides an interface to these algorithms, so that you can directly use Sage symbolic functions as arguments. It is worth mentioning that "out there" you'll find dozens of Python libraries for mathematical optimization. Notice that standard Python packages can be installed with the terminal command command $ sage --pip install --user package_name,

and if the installation goes well, it is available the next time you start Sage via sage: import package_name.

Feel free to provide a minimal example to get more precise feedback.

what optimisation algorithms are available.

Check those provided by SciPy's optimization and root finding library. All of them are available through the Sage interpreter by calling the appropriate scipy.optimize function. In some (but not all) cases, Sage provides an interface to these algorithms, so that you can directly use Sage symbolic functions as arguments.

It is worth mentioning that "out there" you'll find dozens of Python libraries for mathematical optimization. Notice that standard Python packages can be installed with the terminal command

$sage --pip install --user package_name and if the installation goes well, it is available the next time you start Sage via sage: import package_name. Feel free to provide a minimal example to get more precise feedback. what optimisation algorithms are available. Check those provided by SciPy's optimization and root finding library. All of them are available through the Sage interpreter by calling the appropriate scipy.optimize function. For example, to reproduce the one in the basin-hopping algorithm to find the global minimum of a scalar function, sage: import numpy as np sage: from scipy.optimize import basinhopping sage: func = lambda x: np.cos(14.5 * x - 0.3) + (x + 0.2) * x sage: x0=[1.] sage: minimizer_kwargs = {"method": "BFGS"} sage: ret = basinhopping(func, x0, minimizer_kwargs=minimizer_kwargs, niter=200) fun: -1.0008761844426548 lowest_optimization_result: fun: -1.0008761844426548 ...  In some (but not all) cases, Sage provides an interface to these algorithms, so that you can directly use Sage symbolic functions as arguments. It is worth mentioning that "out there" you'll find dozens of Python libraries for mathematical optimization. Notice that standard Python packages can be installed with the terminal command $ sage --pip install --user package_name

and if the installation goes well, it is available the next time you start Sage via import package_name.

Feel free to provide a minimal example to get more precise feedback.

what optimisation algorithms are available.

Check those provided by SciPy's optimization and root finding library. All of them are available through the Sage interpreter by calling the appropriate scipy.optimize function. For example, to reproduce the one in the basin-hopping algorithm to find finds the global minimum of a scalar function,function:

sage: import numpy as np
sage: from scipy.optimize import basinhopping
sage: func = lambda x: np.cos(14.5 * x - 0.3) + (x + 0.2) * x
sage: x0=[1.]
sage: minimizer_kwargs = {"method": "BFGS"}
sage: ret = basinhopping(func, x0, minimizer_kwargs=minimizer_kwargs, niter=200)

fun: -1.0008761844426548
lowest_optimization_result:       fun: -1.0008761844426548
...


In some (but not all) cases, Sage provides an interface to these algorithms, so that you can directly use Sage symbolic functions as arguments.

It is worth mentioning that "out there" you'll find dozens of Python libraries for mathematical optimization. Notice that standard Python packages can be installed with the terminal command

$sage --pip install --user package_name and if the installation goes well, it is available the next time you start Sage via import package_name. Feel free to provide a minimal example to get more precise feedback. what optimisation algorithms are available. Check those provided by SciPy's optimization and root finding library. All of them are available through the Sage interpreter by calling the appropriate scipy.optimize function. For example, the basin-hopping algorithm finds the global minimum of a scalar function: sage: import numpy as np sage: from scipy.optimize import basinhopping sage: func = lambda x: np.cos(14.5 * x - 0.3) + (x + 0.2) * x sage: x0=[1.] sage: minimizer_kwargs = {"method": "BFGS"} sage: ret = basinhopping(func, x0, minimizer_kwargs=minimizer_kwargs, niter=200) fun: -1.0008761844426548 lowest_optimization_result: fun: -1.0008761844426548 ...  In some (but not all) cases, Sage provides an interface to these algorithms, so that you can directly use Sage symbolic functions as arguments. It is worth mentioning that "out there" you'll find dozens of Python libraries for mathematical optimization. optimization (*). Notice that standard Python packages can be installed with the terminal command $ sage --pip install --user package_name

and if the installation goes well, it is available the next time you start Sage via import package_name.

Feel free to provide a minimal example to get more precise feedback.

(*) See eg.: